Machine Learning for Conservation Planning in a Changing Climate

Ana Cristina Mosebo Fernandes, Rebeca Quintero Gonzalez, Marie Ann Lenihan-Clarke, Ezra Francis Leslie Trotter and Jamal Jokar Arsanjani
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Ana Cristina Mosebo Fernandes: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Rebeca Quintero Gonzalez: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Marie Ann Lenihan-Clarke: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Ezra Francis Leslie Trotter: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Jamal Jokar Arsanjani: Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark

Sustainability, 2020, vol. 12, issue 18, 1-28

Abstract: Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.

Keywords: sage-grouse; climate change; machine learning; species distribution modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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